Guillermo Serrano

421 total citations
11 papers, 98 citations indexed

About

Guillermo Serrano is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Guillermo Serrano has authored 11 papers receiving a total of 98 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 2 papers in Computational Theory and Mathematics and 2 papers in Genetics. Recurrent topics in Guillermo Serrano's work include Single-cell and spatial transcriptomics (3 papers), Machine Learning in Bioinformatics (2 papers) and Machine Learning in Materials Science (2 papers). Guillermo Serrano is often cited by papers focused on Single-cell and spatial transcriptomics (3 papers), Machine Learning in Bioinformatics (2 papers) and Machine Learning in Materials Science (2 papers). Guillermo Serrano collaborates with scholars based in Spain, United States and India. Guillermo Serrano's co-authors include Víctor Segura, Elizabeth Guruceaga, Mikel Hernáez, Puri Fortes, Jordi Bruix, Juan P. Unfried, Loreto Boix, Celia Prior, Bruno Sangro and Arián Avalos and has published in prestigious journals such as Bioinformatics, Cancer Research and Journal of Proteome Research.

In The Last Decade

Guillermo Serrano

8 papers receiving 96 citations

Peers

Countries citing papers authored by Guillermo Serrano

Since Specialization

Citations

This map shows the geographic impact of Guillermo Serrano's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Guillermo Serrano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guillermo Serrano more than expected).

Fields of papers citing papers by Guillermo Serrano

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guillermo Serrano. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Guillermo Serrano. The network helps show where Guillermo Serrano may publish in the future.

Co-authorship network of co-authors of Guillermo Serrano

This figure shows the co-authorship network connecting the top 25 collaborators of Guillermo Serrano. A scholar is included among the top collaborators of Guillermo Serrano based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Guillermo Serrano. Guillermo Serrano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

#	Work	Indexed citations
1	Towards a more inductive world for drug repurposing approaches 2025 ·Nature Machine Intelligence ·(unknown), Guillermo Serrano, (unknown), (unknown), (unknown), Marija Pizurica, (unknown), Leonor Puchades‐Carrasco, Antonio Pineda‐Lucena, Idoia Ochoa, Silvestre Vicent, Olivier Gevaert, Mikel Hernáez	0
2	Leveraging multiple labeled datasets for the automated annotation of single-cell RNA and ATAC data 2025 ·Computational and Structural Biotechnology Journal ·(unknown), (unknown), (unknown), (unknown), Olivier Gevaert, Mikel Hernáez, Guillermo Serrano, Idoia Ochoa	0
3	Single-cell dissection of aggression in honeybee colonies 2023 ·Nature Ecology & Evolution ·Ian M. Traniello, Syed Bukhari, Payam Dibaeinia, Guillermo Serrano, Arián Avalos, Amy Cash-Ahmed, (unknown), Mikel Hernáez, Saurabh Sinha, Sihai Dave Zhao, Julian Catchen, Gene E. Robinson	19
4	Ge N Nius : an ultrafast drug–target interaction inference method based on graph neural networks 2023 ·Bioinformatics ·(unknown), (unknown), Guillermo Serrano, Marija Pizurica, (unknown), Antonio Pineda‐Lucena, Silvestre Vicent, Idoia Ochoa, Olivier Gevaert, Mikel Hernáez	9
5	JIND: joint integration and discrimination for automated single-cell annotation 2022 ·Bioinformatics ·(unknown), Guillermo Serrano, Josepmaria Argemí, Ilan Shomorony, Mikel Hernáez, Idoia Ochoa	7
6	SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes 2022 ·Communications Biology ·Jianhao Peng, Guillermo Serrano, Ian M. Traniello, María Eréndira Calleja-Cervantes, Ullas V. Chembazhi, Sushant Bangru, Teresa Ezponda, Juan R. Rodríguez-Madoz, Auinash Kalsotra, Felipe Prósper, Idoia Ochoa, Mikel Hernáez	2
7	Gait Analysis in Child with Dystonic Cerebral Palsy Via Video Photogrammetry Using a Prototype of an Assistive Device 2022 ·International Journal on Advanced Science Engineering and Information Technology ·Guillermo Serrano, (unknown), (unknown), (unknown), (unknown)	0
8	UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm 2020 ·Journal of Proteome Research ·(unknown), Guillermo Serrano, (unknown), Fernando J. Corrales, Elizabeth Guruceaga, Víctor Segura	4
9	Identification of Coding and Long Noncoding RNAs Differentially Expressed in Tumors and Preferentially Expressed in Healthy Tissues 2019 ·Cancer Research ·Juan P. Unfried, Guillermo Serrano, (unknown), (unknown), (unknown), Celia Prior, Loreto Boix, Jordi Bruix, Bruno Sangro, Víctor Segura, Puri Fortes	28
10	DeepMSPeptide: peptide detectability prediction using deep learning 2019 ·Bioinformatics ·Guillermo Serrano, Elizabeth Guruceaga, Víctor Segura	25
11	TransPar: Transformation based dynamic Parallelism for low power CGRAs 2014 ·(unknown), Guillermo Serrano, Masoud Daneshtalab, (unknown), Ahmed Hemani, Kolin Paul, Juha Plosila, Hannu Tenhunen	4

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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